利用多源新闻摘要塑造政治话语
Shaping Political Discourse using multi-source News Summarization
AI总结：
本文提出一种无偏的多文档新闻摘要机器学习模型，通过等量采样主题各维度，生成公正摘要，用于塑造政治话语。
摘要：
多文档摘要是指自动生成与同一主题相关的多篇文档的简洁摘要的过程。该摘要可帮助用户从大量文档中快速理解关键信息。多文档摘要系统比单文档摘要系统更为复杂，因为需要识别并整合来自多个来源的信息。在本文中，我们开发了一种机器学习模型，可从多篇新闻文档中生成某一主题的简洁摘要。该模型设计为无偏见的，通过从该主题的所有不同方面等量采样其输入，即使大多数新闻来源偏向某一方，也能确保公正性。
英文摘要：
Multi-document summarization is the process of automatically generating a concise summary of multiple documents related to the same topic. This summary can help users quickly understand the key information from a large collection of documents. Multi-document summarization systems are more complex than single-document summarization systems due to the need to identify and combine information from multiple sources. In this paper, we have developed a machine learning model that generates a concise summary of a topic from multiple news documents. The model is designed to be unbiased by sampling its input equally from all the different aspects of the topic, even if the majority of the news sources lean one way.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
